Methods of selecting material compositions and designing materials having a target property

ABSTRACT

The disclosed technology relates to a method of selecting a material composition and/or designing an alloy. In one aspect, a method of selecting a composition of a material having a target property comprises receiving an input comprising thermodynamic phase data for a plurality of materials. The method additionally includes extracting from the thermodynamic phase data a plurality of thermodynamic quantities corresponding to each of the materials by a computing device. The extracted thermodynamic quantities are predetermined to have correlations to microstructures associated with physical properties of the material. The method additionally includes storing the extracted thermodynamic quantities in a computer-readable medium. The method further includes electronically mining the stored thermodynamic quantities using the computing device to rank at least a subset of the materials based on a comparison of at least a subset of the thermodynamic quantities that are correlated to the target property.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/887,867, filed Feb. 2, 2018, which is a continuation of U.S.application Ser. No. 14/512,115, filed Oct. 10, 2014, now U.S. Pat. No.10,345,252, which claims the benefit of U.S. Provisional Application No.61/917,845, filed Dec. 18, 2013, now expired, and U.S. ProvisionalApplication No. 61/889,413, filed Oct. 10, 2013, now expired, theentireties of which are hereby incorporated by reference.

BACKGROUND Field of the Invention

The disclosed technology relates in some embodiments to designingmaterials, and more particularly to designing alloys using thermodynamicphase diagrams.

The disclosed technology also relates to selecting compositions ofmaterials, and more particularly to selecting compositions of a materialhaving a target property by using thermodynamic quantities extractedfrom thermodynamic phase data.

Description of the Related Technology

Selecting a material having a target property for manufacturing oftenrequires a manufacturer to have an understanding of the microstructureand/or the nanostructure that is associated with the target property.For some material systems, equilibrium thermodynamics can be used topredict the presence of various phases of a material system underequilibrium conditions. For example, an equilibrium phase diagram can beused to describe physical conditions under which various equilibriumphases of a material system can be stable and under which someequilibrium phases can coexist. Generation of the phase diagrams,however, especially for material systems having many (e.g., greater thanfour) elements with at least as many phases, is oftencomputation-intensive. In addition, when many material systems arecompared for designing a material system, computation and comparison ofthe phase diagrams can be prohibitively costly in terms of bothcomputing and human resources. Furthermore, extraction of usefulinformation often involves a skilled artisan to interpret a graphicalrepresentation, which can also be time-consuming.

Furthermore, while thermodynamic phase diagrams provide equilibriumphase information, they may not necessarily correlate to actual phasespresent because the phase diagrams do not contain information related tokinetics of formation of the phases and/or information related toenergetics related to the microstructure of the materials. Whilekinetics and/or microstructural information can be gathered usingphysical and microstructural analysis techniques such as, for example,electron beam and X-ray imaging and composition analysis techniques,such techniques are also often time consuming and/or cost-prohibitive.

In a manufacturing environment, to select a material composition havinga target property, a material designer can typically analyze a graphicalphase diagram to identify equilibrium phases that may be desirable,synthesize a limited number of samples based on the analysis, andsubsequently perform physical analyses such as electron microscopy andcomposition analysis before choosing the material composition to bescaled up for manufacturing. Such a serial process can be prohibitivelyexpensive and time consuming because the material designer is involvedin the analysis of each graphical phase diagram and/or physical analysisdata to verify whether the synthesized samples do indeed have thedesired phases in the desired amount and in the desired microstructuralform, especially when the material system is complex (e.g., has overfour elements and phases) and many compositions (e.g., hundreds orthousands) are to be evaluated for several target properties. Thus,there is a need for a high throughput method for selecting a materialhaving a target property that is at least partly computer-implementedsuch that the involvement of the material designer can be reduced andeliminated altogether in some portions of the overall selection process.

SUMMARY

In one aspect, a method of selecting a composition of a material havinga target property comprises receiving an input comprising thermodynamicphase data for a plurality of materials. The method additionallyincludes extracting from the thermodynamic phase data a plurality ofthermodynamic quantities corresponding to each of the materials by acomputing device. The extracted thermodynamic quantities arepredetermined to have correlations to microstructures associated withphysical properties of the material. The method additionally includesstoring the extracted thermodynamic quantities in a computer-readablemedium, e.g., a non-transient computer-readable medium. The methodfurther includes electronically mining the stored thermodynamicquantities using the computing device to rank at least a subset of thematerials based on a comparison of at least a subset of thethermodynamic quantities that are correlated to the target property.

In another aspect, a material composition selection apparatus comprisesa thermodynamic phase data extraction module configured to receive aninput comprising thermodynamic phase data for a plurality of materialsand configured to extract therefrom a plurality of thermodynamicquantities corresponding to each of the materials by a computing device.A computing device comprising a processor may also be part of thematerial composition selection apparatus. The extracted thermodynamicquantities are predetermined to have correlations to microstructuresassociated with physical properties of the material. The apparatus mayadditionally include a storage module comprising a non-transitory or anon-transitory medium having stored thereon the extracted thermodynamicquantities. The apparatus further includes an electronic data miningmodule configured to electronically mine the stored thermodynamicquantities using the computing device to rank at least a subset of thematerials based on a comparison of at least a subset of thethermodynamic quantities that are correlated to the target property.

In yet another aspect, a non-transitory computer-readable mediumcomprises instructions stored thereon that when executed cause acomputing device to perform the following steps: receiving an inputcomprising thermodynamic phase data for a plurality of materials;extracting from the thermodynamic phase data a plurality ofthermodynamic quantities corresponding to each of the materials by thecomputing device, wherein the extracted thermodynamic quantities arepredetermined to have correlations to microstructures associated withphysical properties of the material; storing the extracted numericalquantities in a computer-readable medium; and electronically mining thestored thermodynamic quantities using the computing device to rank atleast a subset of the materials based on a comparison of at least asubset of the thermodynamic quantities that are correlated to the targetproperty.

In yet another aspect, a method of designing a material or an alloy isprovided, for example a method for designing a material having a targetproperty. The method comprises calculating thermodynamic phase diagramsfor a plurality of materials or alloys using a processor comprisinglogic circuitry. The method additionally comprises extracting from thephase diagrams numerical thermodynamic quantities corresponding to eachof the plurality of materials or alloys. The method further comprisesstoring the numerical quantities in an electronic database. The methodfurther comprises electronically mining the electronic database or thestored numerical quantities with a processor to rank the materials orthe alloys. The ranking may be based on a comparison of the numericalquantities for different alloy compositions, or the ranking may be basedon a comparison of at least a subset of the numerical quantities foreach material against a material design criteria corresponding to thetarget property.

In some embodiments of the method above, the thermodynamic phasediagrams are calculated to determine equilibrium mole fractions ofthermodynamically stable phases as a function of temperature. In someembodiments, the extracting step is based on a set of predeterminedthermodynamic evaluation criteria. Extracting the thermodynamicquantities may comprise extracting a solidification temperature of atleast one thermodynamically stable phase. Extracting the thermodynamicquantities may comprise extracting a phase transition temperature from afirst phase to a second phase. Extracting the phase transitiontemperature may include extracting a temperature at which a first rateof change of mole fraction of the first Phase as a function oftemperature is negative and a second rate of change of mole fraction ofthe second phase as a function of temperature is positive. Extractingthe thermodynamic quantities comprises extracting an equilibrium molefraction of at least one thermodynamically stable phase at a temperaturebetween about 0° C. and 150° C. Extracting the thermodynamic quantitiesmay comprise extracting a melting temperature, wherein extracting themelting temperature includes extracting a temperature at which a firstrate of change of mole fraction of at least one thermodynamically stablephase is negative and a second rate of change of mole fraction of aliquid phase as a function of temperature is positive.

In some embodiments of the method above, electronically mining maycomprise ranking the materials or alloys based on a comparison ofsolidification temperatures of at least two thermodynamically stablephases. Electronically mining may comprise ranking the materials oralloys based on a comparison of a phase transition temperature from afirst phase to a second phase against at a solidification temperature ofa third phase.

In some embodiments of the method above, storing the numericalquantities may comprise storing in a nonvolatile memory coupled to aprocessor. Storing the numerical quantities may comprise storing in avolatile memory coupled to a processor. Storing the numerical quantitiesmay comprise storing in a removable memory medium.

In some embodiments of the method above, the properties of the materialsor alloys may comprise microstructural properties. The method may beperformed using a computer system comprising a plurality of processors.The entire method may performed using a computer system. The method mayfurther comprise outputting information regarding the ranking of thematerials or alloys. This information may be output to a display or to acomputer-readable medium. The method may further comprise outputting asub-set of materials or alloys having desired properties based on theranking. The method may further comprise manufacturing one or morematerials or alloys from the sub-set of alloys.

In other aspects, a method of designing an alloy need not include stepsof calculating thermodynamic phase diagrams, extracting thermodynamicquantities from the phase diagrams, and storing quantities in anelectronic database. In one aspect, a method of designing an alloy maycomprise electronically mining an electronic database that includesnumerical quantities corresponding to properties of alloys that werepreviously derived from thermodynamic phase diagrams for said alloys,wherein electronically mining is performed with a processor to rank thealloys based on a comparison of the numerical quantities for differentalloy compositions.

In yet another aspect, a method for designing a material having a targetproperty, comprising executing one or more instances of a thermodynamicphase diagram calculation algorithm for a plurality of materials using aprocessor comprising logic circuitry. The method further comprisesexecuting one or more instances of a data extraction algorithm using aprocessor comprising logic circuitry, wherein executing the one or moreinstances of the data extraction algorithm comprises taking as input atleast a subset of results from executing the one or more instances ofthe thermodynamic phase diagram calculation algorithm. The methodfurther comprises storing results from executing the one or moreinstances of the data extraction algorithm in an electronic database.The method further comprises executing one or more instances of a datamining algorithm using a processor comprising logic circuitry, whereinexecuting the one or more instances of the data mining algorithmcomprises taking as input at least a subset of the stored results fromexecuting the one or more instances of the data extraction algorithm.

In some embodiments of the method above, executing the one or moreinstances of the data extraction algorithm comprises extracting from theat least a subset of results from executing the one or more instances ofthe thermodynamic phase diagram calculation algorithm a set of numericalthermodynamic quantities corresponding to each of the plurality ofmaterials, wherein extracting is based on a set of predeterminedthermodynamic evaluation criteria. The results from executing the one ormore instances of the data extraction algorithm may include aspreadsheet including numerical thermodynamic quantities correspondingto each of the plurality of materials. Storing results may includestoring in a nonvolatile storage media. Executing the one or moreinstances of data mining algorithm may include electronically mining thestored results with a processor to rank the materials based on acomparison of at least a subset of the numerical quantities for eachmaterial against a material design criteria corresponding to the targetproperty. One of the processors for executing the one or more instancesof the thermodynamic phase diagram calculation algorithm, the dataextraction algorithm, or the data mining algorithm may be different fromthe remaining ones of the processors. Executing one or more instances ofa data mining algorithm may be performed multiple times from the storedresults.

Other aspects of this disclosure include further computer-implementedmethods related to designing an alloy, as well as systems andapparatuses related to the same, as well as methods of manufacturing analloy and the alloys manufactured themselves.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating a method of selecting a compositionof a material having a target property, according to one embodiment.

FIG. 2 is a schematic illustration of an apparatus for selecting acomposition of a material having a target property, according to oneembodiment.

FIG. 3 illustrates a calculated phase diagram according to oneembodiment.

FIG. 4 is a flow chart illustrating a method of electronically mining aspart of selecting a composition of a material having a target property,according to one embodiment.

FIG. 5A is a chart illustrating a comparison between extractedthermodynamic quantities (calculated phase mole fractions) of a materialand measured thermodynamic quantities (measured phase mole fractions)the material that are correlated to microstructures associated with aphysical property of the material.

FIG. 5B is a scanning electron micrograph showing the microstructures ofthe material associated with the physical property of the material thatwas used to measure the thermodynamic quantities (measured phase molefractions) plotted in FIG. 5A.

FIG. 6A is a chart illustrating an example of the data mining processinvolving 15,000 alloys, according to one embodiment.

FIG. 6B is a chart illustrating another example of the data miningprocess involving 15,000 alloys, according to one embodiment.

FIG. 7 is a flow chart illustrating a method of calculating a phasediagram according to one embodiment.

FIG. 8 is a flow chart illustrating a method of extracting from thephase diagram thermodynamic quantifies as part of selecting acomposition of a material, according to one embodiment.

FIG. 9 is a flow chart illustrating electronically mining a data base aspart of selecting a composition of a material, according to oneembodiment.

FIG. 10 is a flow chart illustrating a method of selecting a compositionof a material, according to one embodiment.

DETAILED DESCRIPTION

Calculation of thermodynamic phase diagrams for selecting a materialcomposition is common practice in the field of metallurgy and materialsscience, and its use, aided by recent advances in computing power, hasdeveloped into a separate field of calculation techniques known in theindustry as Calculation of Phase Diagrams (CALPHAD). The CALPHADtechnique is very useful in aiding the understanding of alloys and inthe design of new alloys. The output of the CALPHAD technique is adiagram displaying certain thermodynamic information such as, e.g., anequilibrium phase diagram which plots, e.g., percent fraction of phasesof a material versus temperature. The displayed diagram is a graphicalrepresentation of the material's thermodynamic information or aplurality of materials' thermodynamic information. The diagram can beused by a skilled artisan to understand alloy systems under equilibriumand to design alloys based on such understanding.

Despite the advances in calculating thermodynamic phase diagrams toquantitatively determine the thermodynamic stability and the presence ofequilibrium phases, as described above, generating and interpreting thephase diagrams, as well as correlating the results to microstructuresassociated with a physical property, can be prohibitively time consumingand/or expensive, especially for complex material systems involving manyelements (e.g., greater than four) and complex microstructures.Disclosed herein are embodiments that describe methods wherebythermodynamic information can be effectively used in such a way that analloy can be designed without a need to repetitively calculate phasediagrams and/or extract thermodynamic quantities therefrom, nor a needto resort to graphically represented phase diagrams, as is often done bya skilled artisan in the industry. Instead, the disclosed embodimentsillustrate generating an alloy database of thermodynamic quantitiescreated from automated algorithms. Subsequent to generation and storageof the thermodynamic quantities in a storage medium, the thermodynamicquantities can be mined by ranking and sorting to select candidates withpredetermined correlation to microstructures associated with a physicalproperty. Advantageously, the mining process can be performed repeatedlyusing automated algorithms, such that many alloys having various targetphysical properties can be designed using the mining process, withouthaving to further calculate or resort to the phase diagrams or any othergraphical display of phase data. The methods described herein areadvantageous in providing rapid material design, because they caneliminate the need for a skilled artisan to evaluate a thermodynamicphase diagram and/or the need to extract thermodynamic quantitiestherefrom each time selection of a material having a new target propertyis desired.

FIG. 1 is a flowchart illustrating a method of selecting a compositionof a material having a target property, according to one embodiment. Themethod 100 of selecting a composition of a material having a targetproperty comprises, at a process 104, receiving an input comprisingthermodynamic phase data for a plurality of materials. The method 100additionally includes, at a process 108, extracting from thethermodynamic phase data a plurality of thermodynamic quantitiescorresponding to each of the materials using a microprocessor. Theextracted thermodynamic quantities may be numerical quantities extracteddirectly from thermodynamic phase data, or numerical quantities that arederived from the directly extracted quantities. The extractedthermodynamic quantities are predetermined to have correlations tomicrostructures associated with physical properties of the material. Themethod 100 additionally includes, at a process 112, storing theextracted thermodynamic quantities in a computer-readable medium. Themethod 100 further includes, at a process 116, electronically mining thestored thermodynamic quantities using a microprocessor to rank at leasta subset of the materials based on a comparison of at least a subset ofthe thermodynamic quantities that are correlated to the target property.

In some embodiments, the process 104 of receiving an input includesloading calculated thermodynamic phase data, e.g., thermodynamic phasediagram data, from computer readable medium such as a storage device ora memory device. In some embodiments, the storage device or the memorydevice from which the input is received at the process 104 may beincluded within a material composition selection apparatus (FIG. 2) inthe form of, for example, an internal storage device or an internalmemory device, for instance a DRAM or an internal storage drive. In yetother embodiments, the input data may be received at the process 104using a portable medium, such as a flash drive or an optical media suchas a CD ROM. In other embodiments, the input data may be received at theprocess 104 via a network from a remote server, for example where thethermodynamic phase data may have been calculated. In yet otherembodiments, the input data may be received at the process 104 via aninput terminal such as a keyboard, an image sensor, a voice sensor and ascanner, among other input terminals through which a user can enterdata.

In some embodiments, the process 116 of electronically mining does notinclude calculating additional thermodynamic phase data or extractingthermodynamic quantities therefrom after storing the extracted numericalquantities.

In some embodiments, the method 100 of selecting the composition doesnot include analysis of a graphical representation of the phase data.

In some embodiments, the method 100 further comprises synthesizing thematerial having a composition corresponding to one of the rankedmaterials.

In some embodiments, the process 108 of extracting comprises executingan algorithm to extract, for each material, the thermodynamic quantitiesselected from the group consisting of a mole fraction of a materialphase at a temperature, a formation temperature of a material phase, adissolution temperature of a material phase, a transition temperaturebetween two phases, a weight percent of an element in a material phaseat a temperature, a mole fraction of a first material phase at atemperature corresponding to a formation temperature or a dissolutiontemperature of a second phase and a weight percent of an element in amaterial phase at a temperature corresponding to a formation temperatureor a dissolution temperature of a second phase. In some embodiments, theprocess 108 of extracting further comprises executing an algorithm tocalculate a quantity derived from one or more of the thermodynamicquantities using a mathematical expression. The mathematical expressioncan be selected from the group consisting of a difference in formationtemperature of two material phases, a difference in dissolutiontemperatures of two material phases, a sum of mole or weight fractionsof at least two material phases at a temperature, a sum of molefractions of at least two material phases at a temperature that arepresent at or below a second temperature.

In some embodiments, the process 116 of electronically mining comprisescomparing at least a subset of the materials in parallel based on atleast a subset of the thermodynamic quantities that are correlated tothe target property.

In some embodiments, the process 116 of electronically mining comprises,before ranking the materials, initially eliminating from an entire setof the plurality of materials one or more material candidates based onone or more criteria selected from a minimum threshold thermodynamicquantity, a maximum threshold thermodynamic quantity and a range betweena minimum threshold thermodynamic quantity and a maximum thresholdthermodynamic quantity. In some embodiments, the process 116 ofelectronically mining comprises ranking the at least the subset ofmaterials after eliminating from an entire set one or more materialcandidates.

In some embodiments, the entire method 100 is performed using a computersystem. In other embodiments, only a subset of the method 100 can beperformed using a computer. For example, predetermining the extractedthermodynamic quantities to be correlated to microstructures associatedwith physical properties of the material can be performed either using acomputer system or performed by a skilled artisan.

In some embodiments, the method 100 is performed using a computer systemcomprising at least one microprocessor. In other embodiments, somefeatures of the method are performed using a subset of themicroprocessors of a computer system while other features of the methodare performed using a different subset of microprocessors of thecomputer system.

In some embodiments, the method 100 further comprises outputtinginformation regarding the ranking of the materials. In otherimplementations, the information is output to a display. In yet otherimplementations, the information is output to a computer-readablemedium.

FIG. 2 is a schematic illustration of an apparatus for selecting acomposition of a material having a target property, according to oneembodiment. The material composition selection apparatus 200 comprises amaterial composition selection module 208, a memory 228, amicroprocessor 232, and a storage 236, which are communicatively coupledto each other via a bus 240. The memory 228 includes one or morevolatile memory devices, such as, for example, a DRAM and/or an SRAM.The storage 236 includes one or more nonvolatile storage devices, suchas magnetic hard drives and/or non-magnetic solid state drives, whichcan in turn include flash memory and/or other nonvolatile memorydevices.

In the illustrated embodiment of FIG. 2, the material compositionselection module 208 is also communicatively coupled to a thermodynamicphase data module 204 and a thermodynamic quantities-microstructurecorrelation module 220. The material composition selection module 208includes a thermodynamic phase data extraction module 212 configured toreceive an input comprising thermodynamic phase data from thethermodynamic phase data module 204, for a plurality of materials andconfigured to extract therefrom a plurality of thermodynamic quantitiescorresponding to each of the materials using the microprocessor 232. Inthe illustrated embodiment, the extracted thermodynamic quantities arepredetermined by the thermodynamic quantities-microstructure correlationmodule 220 to have correlations to microstructures associated withphysical properties of the material. The material composition selectionmodule 208 additionally includes a storage module 216 configured tostore the extracted numerical quantities in a computer-readable medium,which can be one or both of the memory 228 or the storage 236. Thematerial composition selection module 208 further includes an electronicdata mining module 224 configured to electronically mine thethermodynamic quantities stored by the storage module 216 using themicroprocessor 232 to rank at least a subset of the materials based on acomparison of at least a subset of the thermodynamic quantities that arecorrelated to the target property. Each of the thermodynamic quantitiesextraction module 212, the storage module 216 and the electronic datamodule 224 includes specialized algorithms described in the followingthat are implemented on a hardware, which can be at least portions ofthe memory 228, microprocessor 232, and/or the storage 236. In someembodiments, at least portions of the algorithms of the thermodynamicquantities extraction module. 212, the storage module 216 and theelectronic data mining module 224 can be detached from the materialcomposition selection apparatus 200 via a portable storage 236.

In the following, with respect to FIGS. 3, 4, 5A, 5B, 6A and 6B, by wayof example and without losing generality, a concrete example of a methodof selecting a composition of a material having a target property isillustrated.

FIG. 3 illustrates a calculated phase diagram according to oneembodiment. In some embodiments, the method of selecting the compositionincludes receiving an input comprising thermodynamic phase data for aplurality of alloys where each alloy is a multi-phase system. In someembodiments, thermodynamic phase diagrams can be calculated using asuitable method, for example, using a method of Computer Calculations ofPhase Diagrams (CALPHAD). In embodiments employing the CALPHAD method, acomputer system uses a mathematical model to calculate Gibbs free energycurves of the individual phases of an alloy composition. For some phasesof the material system, an analytical expression for calculating theGibbs free energy may not exist. Therefore, the Gibbs free energy curvesare calculated using the computer system by fitting mathematical modelsto experimental data using adjustable parameters. The adjustableparameters may be retrieved from a computer storage system.Subsequently, the Gibbs energy curves of the individual phases can becombined to describe a multi-phase alloy system. In some embodiments,the calculation of phase diagram using the CALPHAD method can beimplemented in commercially available software packages such asThermo-Calc (thermocalc.com/).

In some embodiments, a typical alloy system has at least four elements.The calculated phase diagram 310 is for an example composition of anAlloy System 1 having a composition (in wt. %) ofFe_(bal)B_(1.3)C_(0.8)Cr₅MmMo₁Nb₄Si_(0.5)Ti_(0.5)V_(0.5). The phasediagram 310 displays equilibrium mole fractions of thermodynamicallystable phases on the y axis as a function of temperature displayed onthe x-axis. FIG. 3 includes mole fraction curves of stable phases of thecomposition of the Alloy System 1 including phases, of liquid 312 andfirst through ninth phases that are, in the illustrated embodiment, TiB₂314, NbC 316, austenite 318, ferrite 320, (Fe,Cr)-(C,B)-1 322,(Fe,Cr)-(C,B)-2 324, Mo₃B₂ 326 and (Fe,Cr)23(C,B)6 328.

In some embodiments, a method of designing an alloy includes extractingfrom the phase diagrams thermodynamic quantities corresponding to eachof the plurality of alloys, where the thermodynamic quantities comprisenumerical quantities that correspond to properties of the alloys. Insome embodiments, the thermodynamic quantities comprise single numericalquantities. In other embodiments, the thermodynamic quantities comprisequantities derived from the single numerical quantities using analgorithm.

Still referring to FIG. 3, in some embodiments, extracting thethermodynamic quantities comprises extracting a solidificationtemperature of at least one thermodynamically stable phase. For example,in FIG. 3, the solidification temperatures of thermodynamically stablephase includes a solidification temperature 330 of a primary carbide,which can include NbC, and solidification temperatures 332 of grainboundary carbides, which can include (Fe,Cr)-(C,B)-1, (Fe,Cr)-(C,B)-2,and (Fe,Cr)23(C,B)6.

Still referring to FIG. 3, in some embodiments, extracting thethermodynamic quantities comprises extracting a phase transitiontemperature from a first phase to a second phase. For example, in FIG.3, extracting the thermodynamic quantities includes extracting a phasetransition temperature 334 corresponding to a phase transitiontemperature from an austenite phase to a ferrite phase. While the phasetransition temperature 334 in this example refers to a temperature atwhich percent mole fractions of austenite and ferrite phases are aboutequal, the phase transition temperature can be extracted anywhere froman overlapping region between the mole fraction curves of the austenitephase 318 and the ferrite 320, where a first rate of change of molefraction of the ferrite phase 320 as a function of temperature isnegative and a second rate of change of mole fraction of the austenitephase 318 as a function of temperature is positive. A rate of change canbe represented for example by dc/dT, where dc is a change in percent ofmole fraction of a phase and dT is a change in the temperaturecorresponding to the change in the percentage of mole fraction of thephase.

Still referring to FIG. 3, in some embodiments, extracting thethermodynamic quantities comprises extracting an equilibrium molefraction of at least one thermodynamically stable phase at a specifiedtemperature. For instance, in FIG. 3, equilibrium mole fractions 336 caninclude equilibrium mole fractions between a first temperature and asecond temperature (about 0° C. and 100° C. in the illustrateembodiment) of liquid 312, TiB₂ 314, NbC 316, austenite 318, ferrite320, (Fe,Cr)-(C,B)-1 322, (Fe,Cr)-(C,B)-2 324, Mo₃B₂ 326, and(Fe,Cr)23(C,B)6 328. In addition, a minor phase fraction 342precipitating during re-heat can be extracted within a temperature range(between a third temperature and a fourth temperature of about 680° C.and 800° C., respectively, in the illustrated embodiment).

Still referring to FIG. 3, in some embodiments, extracting thethermodynamic quantities comprises extracting a melting temperature,wherein extracting the melting temperature includes extracting atemperature at which a first rate of change of mole fraction of at leastone thermodynamically stable phase is negative and a second rate ofchange of mole fraction of a liquid phase as a function of temperatureis positive. For example, while in FIG. 3, melting temperature 338corresponds to a temperature at which the percent mole fractions of theliquid and austenite phases 312 and 318 are about equal, the meltingtemperature can be extracted anywhere within an overlapping regionbetween the mole fraction curves of the liquid phase 312 and any otherphase, in which a first rate of change of mole fraction of at least onethermodynamically stable phase is negative and a second rate of changeof mole fraction of the liquid phase 312 as a function of temperature ispositive.

In some embodiments, calculating a phase diagrams and extractingthermodynamic quantities from the phase diagram are run iteratively foreach of the plurality of alloy compositions.

As an illustrative example, the extracted thermodynamic quantities mayinclude: 1) phase fraction of NbC at 100° C., 2) solidificationtemperature of NbC, 3) solidification temperature of (Fe,Cr)-(C,B)-1, 4)solidification temperature of (Fe,Cr)-(C,B)-2, 5) phase fraction of(Fe,Cr)-(C,B)-1 at 100° C., and 6) phase fraction of (Fe,Cr)-(C,B)-2 at100° C. The thermodynamic quantities may be iteratively extracted forthe Alloy System 1 where the concentration of B is varied from 0.5 to2.0 percent in steps of 0.5 percent, for a total of six alloys, and Tiis varied from 1 to 5 percent in steps of 0.5 percent, as an example.

In some embodiments, the method of selecting an alloy compositionincludes storing at least a subset of the numerical quantities extractedas described above in an electronic database. The numerical quantitiesthat are stored represent a streamlined set of numerical quantities thatare predetermined to have a correlation to certain microstructuralproperties. For example, the numerical quantities may be correlated tothe presence of matrices and precipitates having specific phases of thealloy system. The microstructural properties can in turn be correlatedto certain end material properties such as hardness, fracture toughness,magnetic permeability, etc.

The storage medium can include any suitable storage medium configured tostore information with or without power supplied to the medium,including a volatile memory medium such as a DRAM and an SRAM, and/or anonvolatile medium such as a flash memory or a disk drive. In someembodiments, the storage medium includes a removable storage media, suchas a removable hard drive or a removable flash drive.

It will be appreciated that while it is possible to use techniques suchas the CALPHAD method to calculate a phase diagram, a determination ofwhich of the massive amount of information contained in the phasediagram are relevant in determining end material properties. Forexample, while the calculated phase diagram in FIG. 3 above shows a highfraction of high temperature forming NbC phase and no (Fe,Cr)-(C,B)phase, which forms above the austenite to ferrite transitiontemperature, the predetermination of these quantities as they relate tocertain microstructural and material properties takes an understandingof experimental and theoretical physical metallurgy.

It will be appreciated that extracting thermodynamic quantities asdescribed above can take a prohibitive amount of time and calculationresource without using the method described herein. For example, asingle mole fraction curve of each stable phase in FIG. 3 comprises atleast 30 individual data points. Without using a computer, it would takea person having ordinary skill in the art using a calculator, forexample, at least several minutes per each data point. For an alloyhaving several phases such as in FIG. 3, calculation of mole fractioncurves for all stable phases could take at least several hours. In orderto calculate a system of alloys having several to several tens ofcompositions, extracting thermodynamic quantities can take days toweeks, if not longer. Using the methods described herein, similarcalculations for an alloy system having several to several tens ofcompositions can be completed in several minutes to several hours. Insome embodiments, over 1000 alloy compositions can be calculated inabout two days.

The streamlined storage of predetermined numerical quantities asdescribed above enables a fast retrieval of relevant information for ahigh throughput analysis. A typical analysis using the present methodcan be performed >1,000 times faster than conventional methods such asCALPHAD methods. This is because conventional methods utilize largethermodynamic databases, which utilize computationally expensiveformulas to generate massive amounts of thermodynamic information. Incontrast, the electronic database created in the present method issimply a series of numbers tied to alloy composition, which can bereferenced, ranked, and used for alloy design in very short times.

An example set of stored numerical quantities is shown in the TABLE 1that can be generated by the computing system. As noted above, it willbe appreciated that while the values below may be inherently containedwithin a phase diagram, it takes a skilled metallurgist running a seriesof physical experiments (alloy manufacture, metallography, propertymeasurement) to have predetermined that the numerical quantities have acorrelation to certain material properties such as a desiredmicrostructure that are in turn correlated to an end material property.

TABLE 1 Phase % NbC Austenite FCC to (Fe,Cr)-(C,B) Alloy NbC Solidfy TSolidify T BCC T Solidify T 1 10 1600 1300 800 1200 2 5 1400 1350 9501100 3 3 1500 1250 875 800 4 2 1100 1200 700 650

In some embodiments, the method of designing an alloy includeselectronically mining the electronic database with a processor to rankthe alloys based on a comparison of the numerical quantities fordifferent alloy compositions. In some embodiments, the numericalquantities used to rank the alloys can be based on a subset ofthermodynamic quantities that are extracted as described above. Themining process comprises referencing the specific thermodynamicquantities that have been predetermined to be correlated to usefulmicrostructural and material properties as described above.

The described mining concept is an alloy design concept, which isseparate and unique from utilizing a computer to execute thermodynamiccalculations alone. In conventional CALPHAD techniques, the phasediagram is directly referenced by the metallurgist to understand alloybehavior. In this invention, the phase diagram is not referenced by themetallurgist, rather the user directly references the minedthermodynamic data for alloy design. This difference is unique andallows for one skilled in the art to evaluate the behavior of manyalloys simultaneously and allows for one unskilled in the art to performalloy design.

In some embodiments, electronically mining comprises ranking the alloysbased on a subset of the numerical quantities stored in the electronicdatabase. For example, referring back to TABLE 1, while all numericalquantities in TABLE 1 may be stored in a storage medium, a subset of thestored numerical quantities may be used for ranking the alloys. Forexample, the subset may include numerical quantities of Phase % NbC butexclude one or more of NbC solidification temperature (NbC solidify T),Austenite solidification temperature (Austenite Solidify T), FCC to BCCtransition temperature (FCC to BCC T) and (Fe,Cr)-(C,B) solidificationtemperature ((Fe,Cr)-(C,B) Solidify T).

In some embodiments, electronically mining comprises ranking the alloysbased on a comparison of solidification temperatures of at least twothermodynamically stable phases. For example, referring back to FIG. 3,alloys may be ranked based on a comparison between a solidificationtemperature 330 of a primary carbide (e.g., NbC) and a solidification ofthe austenite 318.

In some embodiments, electronically mining comprises ranking the alloysbased on a comparison of a phase transition temperature from a firstphase to a second phase against at a solidification temperature of athird phase. For example, referring back to FIG. 3, alloys may be rankedbased on a comparison between the phase transition temperature 334corresponding to a phase transition temperature from an austenite phaseto a ferrite phase, and solidification temperature 332 of grain boundarycarbides (e.g., (Fe,Cr)-(C,B)-1 322, (Fe,Cr)-(C,B)-2 324 and(Fe,Cr)23(C,B)6 328.

FIG. 4 is a flow chart illustrating a method of electronically mining aspart of selecting a composition of an alloy having a target property,according to one embodiment. The mining process 450 includes a process452 of starting to evaluate an alloy. The process 452 can includeretrieving, for example, a set of stored numerical quantities of analloy composition as described above with respect to TABLE 1.

The mining process 450 additionally includes determining at a process454 whether a solidification temperature of a first phase, e.g., an NbCphase, is greater than solidification temperature of a second phase,e.g., an FCC phase.

Once the solidification temperature of the NbC phase is found to begreater than the solidification temperature of the FCC phase, the miningprocess proceeds to determining at a process 456 whether the a phasetransition temperature from the FCC to a third phase, e.g., a BCC phaseis greater than a solidification temperature of a fourth phase, e.g., a(Fe,Cr)-(C,B) phase.

On the other hand, if the solidification temperature of the NbC phase isfound to be less than or equal to the solidification temperature of theFCC phase at the process 456, the mining process proceeds to determiningat a process 458 whether there are additional alloys remaining in thedatabase.

Once the phase transition temperature from the FCC to the BCC phase isdetermined to be greater than a solidification temperature of the(Fe,Cr)-(C,B) phase at the process 456, the data mining process 450proceeds to a process 460 where a unit of measure for the alloy isrecorded as a function of mole percent of the NbC phase. The unit ofmeasure, for example, can be at least one of the NbC solidificationtemperature, the FCC solidification temperature, the phase transitiontemperature from the FCC to the BCC phase, and the solidificationtemperature of the (Fe,Cr)-(C,B) phase.

On the other hand, if the phase transition temperature from the FCC tothe BCC phase is determined not to be greater than a solidificationtemperature of the (Fe,Cr)-(C,B) phase at the process 456, the miningprocess 450 proceeds to determining at a process 458 whether there areadditional alloys remaining in the database. At the process 458, if itis determined that additional alloys remain in the database to beevaluated, the mining process 450 starts another process 452 of startingto evaluate an additional alloy. On the other hand, at the process 458,if it is determined that no additional alloys remain in the database,the mining process 450 ranks the evaluated alloys according to the unitof measure.

In the foregoing, the method for designing an alloy was described in thecontext of calculation of equilibrium phase diagrams as a starting pointand obtaining thermodynamic quantities therefrom. However, theembodiments described herein can apply to calculation of othercalculations, including: calculations of chemical driving forces,CVD/PVD deposition simulations, CVM calculations of ordering/disorderingphenomena, Scheil-Gulliver solidification simulations, liquidus andsolidus surface projections, Pourbaix diagrams, Ellingham diagrams,partition coefficients, and partial gas pressures, among othercalculations.

FIGS. 5A and 5B illustrate, by way of example and without loss ofgenerality, correlating the extracted thermodynamic quantities of amaterial to microstructures associated with a target physical propertyof the material. FIG. 5A is a comparison bar graph 500 comparingextracted thermodynamic quantities (calculated phase mole fractions)504, 512, 520, 528 of a material and measured thermodynamic quantities(measured phase mole fractions from an ingot) 508, 516, 524 and 532 thatare correlated to microstructures associated with a physical property ofthe material. FIG. 5B is a scanning electron (SEM) micrograph 540showing the microstructures of the material associated with the physicalproperty of the material that was used to obtain the measured thethermodynamic quantities in FIG. 5A. The comparison bar graph 500 isthat of a particular alloy FeB_(1.4)C_(0.8)Cr₅Mo₁Nb₄Ti_(0.5)V_(0.5), andcompares the calculated phase mole fractions 504, 512, 520 and 528 offerrite, austenite, a secondary carbide and a primary carbide,respectively, against respective measured phase mole fractions 508, 516,524 and 532 of ferrite, austenite, a secondary carbide and a primarycarbide, respectively. The phase mole fractions for the illustratedexample were obtained by analyzing the SEM micrograph 540 of FIG. 5B.Microstructural regions 550 and 560 of the SEM micrograph 540 correspondto the primary and secondary phases, in the illustrated example. It willbe appreciated that the calculated and measure amounts of phase molefraction are not the same, and an offset relationship can be a factorthat is taken into consideration at a later mining stage. In theillustrated example, the inventors determined that the target propertiesof simultaneous high crack resistance and high wear resistance arecorrelated to the measured phase mole fractions 524 and 532 of thesecondary carbide and the primary carbide, respectively. Furthermore,the microstructural locations of these phases were also determined to becorrelated to the target properties. Based on this microstructuralknowledge of the correlations between the thermodynamic quantities andthe microstructures associated with the physical properties, theextracted thermodynamic phase data can later be mined for the specificphysical properties. These advantages are described in more detail withrespect to EXAMPLE 2, described below.

FIG. 6A is a chart 600 illustrating an example of the electronic datamining process involving extracted thermodynamic quantities 604 of15,000 alloys, according to an embodiment. The y axis represents a firstthermodynamic quantity associated with a Cr content level in theaustenite phase, and the x axis represents a second thermodynamicquantity associated with a secondary carbide content level. Based on acorrelation between the thermodynamic quantities and microstructuresassociated with a target property in a similar manner as described abovewith respect to FIGS. 5A and 5B, the data base containing the extractedthermodynamic quantities can be mined for a specific combination offirst and second thermodynamic quantities. Additional description ofthis process is provided below with respect to EXAMPLE 3.

FIG. 6B is a chart 620 illustrating another example of the electronicdata mining process involving extracted thermodynamic quantities624a-624k of 15,000 alloys, according to an embodiment. The x axisrepresents a first thermodynamic quantity associated with an FCC-BCCphase transition, and the y axis represents a second thermodynamicquantity associated with a primary carbide content level. Based on acorrelation between the thermodynamic quantities and microstructuresassociated with a target property in a similar manner as described abovewith respect to FIGS. 5A and 5B, the data base containing the extractedthermodynamic quantities can be mined for a specific combination offirst and second thermodynamic quantities. Additional description ofthis process is provided below with respect to EXAMPLE 3.

It will be appreciated that the results of both FIG. 6A and FIG. 6B canbe obtained after the extraction process with no additionalthermodynamic quantities extraction and no additional calculation ofphase data. That is, a single data extraction process can be sufficientfor multiple mining processes to determine material compositions for avariety of different target properties, which can be entirelyindependent of one another.

As discussed above, the method of designing an alloy according toembodiments herein are best implemented using an electronicallyimplemented system including a processor comprising logic circuitry.FIGS. 7-10 illustrate embodiments of algorithms that can be executed onthe system.

FIGS. 7-10 illustrate a method for designing a material, e.g., an alloy,having a target property, including calculating thermodynamic phasediagrams for a plurality of materials using a processor comprising logiccircuitry (FIG. 7), extracting from the phase diagrams numericalthermodynamic quantities corresponding to each of the plurality ofmaterials, wherein extracting is based on a set of predeterminedthermodynamic evaluation criteria (FIG. 8), and electronically miningthe stored numerical quantities with a processor to rank the materialsbased on a comparison of at least a subset of the numerical quantitiesfor each material against a material design criteria corresponding tothe target property (FIG. 9).

FIG. 7 is a flow chart illustrating a phase diagram calculationalgorithm 100 for designing an alloy according to one embodiment,including calculating thermodynamic phase diagrams for a plurality ofalloys using a processor comprising logic circuitry. In someembodiments, the algorithm depicted in FIG. 7 can be implemented as astand-alone algorithm. In other embodiments, the algorithm 700 can beimplemented as a subroutine, i.e., as part of a larger algorithm.

In the illustrated embodiment of FIG. 7, the phase diagram calculationalgorithm 700 includes various processes including, at the beginning, aprocess 704 for selecting elements and specifying composition and/ortemperature ranges and step sizes. For example, if carbon is specifiedas an element, a composition range from Min=0% to Max=1%, to becalculated at step increments of 0.1%, may be specified at the process704. In addition, a temperature range from 300K to 2000K, for example,to be calculated at step increments of 50K, may be specified at theprocess 704.

Still referring to FIG. 7, the phase calculation algorithm 700additionally includes a process 708 for setting an alloy composition. Atthe process 708, one alloy composition within the composition rangespecified in the process 704 may be set for calculation. For example,Fe_(bal)B_(1.3)C_(0.8)Cr₅Mn₁Mo₁Nb₄Si_(0.5)Ti_(0.5)V_(0.5), may be aspecific composition that can be set at the process 708 for setting thealloy composition. The phase diagram calculation algorithm 700additionally includes a process 712 for setting a temperature within thetemperature range specified in the process 704. For example, the firsttemperature to be calculated can be the minimum temperature value withinthe temperature range selected in the process 704.

Although not shown for clarity, in some embodiments, additionalthermodynamic parameters may be set in addition to the temperature atthe process 712, for example, to further reduce the degrees of freedomto zero. As used herein, the degree of freedom refers to the number ofintensive properties such as temperature or pressure, which areindependent of other intensive variables. The degree of freedom may beexpressed, for example, by the Gibbs' phase rule, which states thatF=C−P+2, where C is the number of components and P is the number ofphases.

Still referring to FIG. 7, phase calculation algorithm 700 additionallyincludes Calculating at a process 716 a phase equilibrium parameter or aset of phase equilibrium parameters, such as, for example, the molefractions of the phases present at the temperature specified at thetemperature setting process 712.

Still referring to FIG. 7, upon completion of the calculation of thephase equilibrium parameter at process 716, the phase calculationalgorithm 700 proceeds to a decision process 720 to determine whetherthe phase equilibrium parameter last calculated at the process 716corresponds to the last temperature of the full temperature rangeselected at the process 704. Upon determining at the process 720 thatthe calculation at the process 716 does not correspond to the lasttemperature of the range selected at the process 704, the algorithm 700increments the temperature by a step size set at the process 704. Forexample, the temperature may be increased from 300 K to 350 K if thetemperature step size is specified as 50 K at the process 704. Thealgorithm then loops back to the process 712 to calculate the next setof phase equilibrium parameters, e.g., mole fractions, at the newly settemperature value. The iterative loop continues until the fulltemperature range set at the process 704 has been calculated.

Still referring to FIG. 7, upon determination that the full temperaturerange has been calculated at the decision process 720, the phase diagramcalculation algorithm 700 proceeds to store at a process 724, in anindividual alloy data file, the calculated phase equilibrium parametersfor the composition set at the process 708, for the full temperaturerange selected at the process 704. The stored alloy data may be in atabulated form, for example, which can be stored as multiplespreadsheets with relevant thermodynamic information for an alloydesign. For example, the first sheet may contain the mole fraction ofeach phase present in the alloy at all the calculated temperatures.Additional sheets may, for example, contain information such as thechemical composition of each present phase at all calculatedtemperatures.

Still referring to FIG. 7, after the individual alloy data file has beenstored at the process 724, the phase diagram calculation algorithm 700proceeds to determine at the decision process 728 whether the full rangeof the alloy composition specified at the process 704 has beencalculated. Upon determination that the full range of the alloycomposition has not been calculated, the algorithm 700 loops back to theprocess 708, where a new alloy composition is set and processes 708 to724 are iteratively repeated until a determination is made at thedecision process 728 that the full range of the alloy composition hasbeen calculated. In some embodiments, the composition of one element canbe varied for each loop from the process 708 to the process 728. Inother embodiments, compositions of more than one (e.g., two or three)alloying elements can be varied for each loop. For example, after thefull temperature range for an alloy has been calculated for a carboncontent of 1 wt. %, the next alloy calculated can have a carbon contentof 1.5 wt. % for a step size specified to be 0.5 wt. % carbon. Thecorresponding weight percent of the solvent element is thereby reducedby 0.5 wt. %, such that the composition of more than one alloyingelements are varied for each loop. However, the algorithm can bedesigned to calculate more complex alloying variations if desired.

Still referring to FIG. 7, after the last individual alloy data file hasbeen stored at the process 724 and a determination is made at thedecision process 728 that the full range of the alloy composition hasbeen calculated, the phase diagram calculation algorithm ends at the aprocess 732. In one example, upon completion of the phase diagramcalculation algorithm 700, a data folder comprising individual files foreach calculated alloy composition can be generated and stored.

It will be appreciated that in some embodiments, the phase diagramcalculation algorithm 700 is automated such that the algorithm 700 isconfigured to take human input only at the process 704 for selectingelements and specifying calculation ranges and step sizes, such that thesubsequent processes 708-732 can be performed, and the results stored,automatically for the entire set of elements over the entire calculationranges specified at the process 704. It will be further appreciated thatthe amount of data obtained for a typical calculation is practicallyprohibitive to calculate or handle without an algorithm such as thealgorithm 700 implemented in an electronically implemented systemincluding a processor, as described herein. By way of an illustrationonly, an Fe-based alloy having the following elements can be considered:carbon (C), boron (B), titanium (Ti) and niobium (Nb). For example, thecompositions for C and B can be selected to have a range between 0 and 1wt. %, and the composition step size can be set at 0.1 wt. %.Additionally, the compositions for Nb and Ti can be selected to have arange between 0 and 10 wt. %, and the composition step size can be setat 1 wt. %. Additionally, the temperature can be selected to have arange between 300K and 2,000K, and the step size can be set at 50K. Sucha range, which may be considered relatively coarse by a person havingordinary skill in the art for designing commercial alloys, can alreadyyield a prohibitive amount of data for calculating and handling withoutan algorithm implemented in an electronic system including amicroprocessor. To illustrate, calculation in this example would involvea data set including 11×11×11×11=14,641 different alloy compositions(i.e., 0-10 wt. % and 0-1 wt. % produces 11 different iterations withthe given step sizes). In addition, for the specified temperature rangeand assuming a reasonable value of 5 phases present in each alloy, eachalloy would contain 35×5 (phase mole fraction data)+5×4×35 (phasechemistry data=875 data points per alloy composition. In sum, the entiresub-routine would have stored 14,641×875=10,248,875 data points, storedin 14,641 individual alloy data files.

Data extraction involves the compilation of relevant thermodynamicquantities from a phase diagram. The selection of this thermodynamicquantity must be executed by one skilled in the art of metallurgy, basedon experimental measurements, for the purposes of predicting themicrostructure and performance of calculated alloys. The thermodynamicquantities extracted from the phase diagram are not obviously present orapparent in the phase diagram itself. An additional calculation routinemust be written and executed for each unique thermodynamic quantity ofinterest.

In one example the phase fraction is a desired thermodynamic quantity.As the phase fraction of each phase in a phase diagram changes and isthereby a function of temperature in addition to other variables, askilled metallurgist must execute experimental trial in order todetermine how to control these variables in order to extract theappropriate phase fraction as a numerical quantity for alloy design. Inthis and other examples, a separate algorithm must be written to extractthe appropriate thermodynamic quantities.

In other examples, the thermodynamic quantities, which are extracted,are not present in the phase diagram at all, but rather are mathematicalexpressions of the information calculated from the information in thephase diagram. Similarly, a unique calculation routine must be writtenand executed to calculate and store a piece of numerical information,which is not present in the original phase diagram.

The above example illustrates that the extraction routine and the uniquealgorithms required to generate the thermodynamic quantities are not amere rearrangement of the information present in the original phasediagram, rather it is the generation of new thermodynamic quantitieswhich have additional benefit beyond the phase diagram alone in terms ofexecuting alloy design.

The extraction step generates a new database which ties each alloy toeach thermodynamic criteria. This database will act as the input for thedata mining algorithms which is the actual stage of alloy design.

From the relatively vast amount of data, in the following, extracting asubset of thermodynamic quantities is described. FIG. 8 is a flow chartillustrating a data extraction algorithm 800 for designing an alloyaccording to one embodiment, including extracting from the phasediagrams numerical thermodynamic quantities corresponding to each of theplurality of materials, wherein extracting is based on a set ofpredetermined thermodynamic evaluation criteria, using a processorcomprising logic circuitry. In some embodiments, the algorithm 800depicted in FIG. 8 can be implemented as a stand-alone algorithm. Inother embodiments, the algorithm 800 can be implemented as a subroutine,i.e., as part of a larger algorithm.

Still referring to FIG. 8, in some embodiments, the data extractionalgorithm 800 can be configured to take as input the individual alloydata files created as a result of implementing the phase diagramcalculation algorithm 800 of FIG. 8. The algorithm 800 includes variousprocesses, including selecting alloys and evaluation criteria at process804 at the beginning. In some embodiments, the process 804 may beperformed manually, and may represent the only manual process among theprocesses included in the algorithm 800. The process 804 includesselecting one or more alloys, e.g., one or more alloys calculated in thephase diagram calculation algorithm 800 of FIG. 8. Furthermore, theprocess 804 includes specifying one or more evaluation criteria, whichcan be thermodynamic criteria by which the one or more alloys are to beevaluated. By way of an example, referring back to the exampleillustrated in TABLE 1, the five different criteria including phase % ofNbC, NbC solidification temperature, austenite solidificationtemperature, FCC to BCC transition temperature, and (Fe,Cr)-(C,B)solidification temperature shown in TABLE 1 can represent thethermodynamic criteria selected at the process 804.

Still referring to FIG. 8, once the alloys and evaluation criteria areselected at the process 804, the algorithm 800 proceeds to open at aprocess 808 an individual data file corresponding to one of theindividual alloys selected at process 804. Referring back to theFe-based alloy example discussed in connection with FIG. 8 by way ofillustration, the individual data file to be opened at process 808 maybe one of the 14,641 individual alloy data files calculated as inexecuting the phase diagram calculation algorithm 700 in FIG. 7.

Still referring to FIG. 8, after opening the individual data filecorresponding to the one of the individual alloys selected at process808, the algorithm 800 proceeds to perform an analysis calculation at aprocess 812 on the individual data file for the alloy to evaluate thedata file against the evaluation criteria (e.g., thermodynamic criteria)selected at the process 804. Referring back to the example of TABLE 1,the algorithm can, for example, scan the data points in the individualalloy file to determine parameters corresponding to each of the fivethermodynamic criteria. The result of each analysis calculation mayrepresent TABLE 1, for example.

Still referring to FIG. 8, after each performance at the process 812 ofperforming the analysis calculation, a determination is made at adecision process 816 as to determine whether all evaluation criteriahave been evaluated for the individual data file. Upon determinationthat there are evaluation criteria remaining to be analyzed on the datafile, the algorithm 800 loops back to the process 812 to performadditional analysis calculations on the data file iteratively until allevaluation criteria selected at the process 804 have been evaluated onthe data file. Referring back to TABLE 1 by way of an example, theprocess loop between processes 812 and 816 continues until all fivethermodynamic criteria listed in the first row of TABLE 1 have beencalculated for the alloy represented by one of the rows.

Still referring to FIG. 8, once all evaluation criteria have beendetermined to have been evaluated at the decision process 816, thealgorithm 800 proceeds to store the results of the calculation in aseparate tabulated electronic file at a process 820. In one example,this can be in the form of a spreadsheet file. Referring back to theexample of TABLE 1, the tabulated electronic file may be in a formatsimilar to TABLE 1.

Once the results of the analysis calculations for an individual alloyhas been tabulated and stored at the process 820, the algorithm 800proceeds to a decision process of 824 to determine whether all of thealloys selected in the process 804 have been evaluated and theircorresponding data stored. Upon determination that there are alloysremaining to be evaluated, the algorithm 800 loops back to the process808 to open another individual alloy data file and performs the processloop from 808 to 820 continues until all alloys selected at process 804have been evaluated, at which point the algorithm 800 proceeds toprocess 828 to end the data extraction algorithm 800.

Upon completion of the data extraction algorithm 800, a streamlined dataset extracted from the initially much larger data set resulting from thephase diagram calculation algorithm 700 can be obtained and stored in asingle streamlined data storage file, such as for example, a spreadsheetfile similar in format to TABLE 1.

It will be appreciated that upon completion of the data extractionalgorithm 800, the complex information contained within a phase diagramhas been simplified into a set of discrete numerical quantities whichcan be further interpreted and evaluated using computational methods.For example, referring back to the previous example discussed inconnection with FIG. 7 where 14,641 alloys have been calculated,implementation of the data extraction algorithm 800 on such data setstreamlines vast amounts of thermodynamic information contained in14,641 individual files to extract a single spread sheet summarizing thealloy compositions against key evaluation criteria. It will be furtherappreciated that while the calculation of all 14,641 alloys may take upto two weeks using a continuously running computer, the data storagestep of the same quantity of alloys may take only several hours. Thequantified information contained in this sheet can then be easilymanaged by a data mining algorithm, described below.

The mining method is an independent routine from the extraction method.For example, after one or more alloys have been calculated and thisalloy set has been run through the extraction routine, multiple miningroutines can be run using the extracted data without repeating thecalculation or extraction steps again. Again, this marks a cleardistinction between using a computer to execute the CALPHAD process. Inthis conventional method the computer is used to calculate phasediagrams, which a metallurgist can use for alloy design. Additionalalloy design steps using computer based CALPHAD again requiresadditional calculations and/or evaluations of a phase diagram. In thisinvention, phase diagrams need not be calculated again for multipledesign efforts and the metallurgist does not interface with the phasediagrams directly to execute alloy design. Rather he can continuouslymine the newly developed database of thermodynamic quantities to designalloys. In this invention, the user can utilize the advantages of thecomputer based approach, but does not require additional calculations orinterfacing with any phase diagrams for each unique alloy designconcept.

The data mining steps enables another fundamental difference betweentraditional CALPHAD and computer assisted CALPHAD methods in that itenable alloy design without the use of a chart, plot, diagram or anydisplay of thermodynamic information whereby one skilled in the art ofmetallurgy must interpret. The data mining stage executes alloy designthrough purely numeric and algorithmic evaluation. This method isbeneficial for several reasons, 1) it is purely objective, no inherentknowledge of alloy behavior is required for design, 2) one who is notskilled in the art of metallurgy can execute alloy design based on aseries of sorting and ranking steps.

For example, the extraction step may create a database of 100 alloysties to 20 unique thermodynamic variables. At no stage is it necessaryto plot the thermodynamic information into a visual or graphical formatin order to execute alloy design. Rather, the thermodynamic parametersor a subset of those thermodynamic parameters can be used to sort andrank the alloys for the purposes of design.

FIG. 9 is a flow chart illustrating a data mining algorithm 900 fordesigning an alloy according to one embodiment, including electronicallymining the stored numerical quantities with a processor to rank thematerials based on a comparison of at least a subset of the numericalquantities for each material against a material design criteriacorresponding to the target property. The data mining algorithm 900 canbe implemented using a processor comprising logic circuitry. In someembodiments, the algorithm depicted in FIG. 9 can be implemented as astand-alone algorithm. In other embodiments, the algorithm 900 can be asubroutine, i.e., part of a larger algorithm.

Referring to FIG. 9, in some embodiments, the data mining algorithm 900can be configured to take as input the stored data resulting from thedata extraction algorithm 800 of FIG. 8. In FIG. 9, the data miningalgorithm 900 is initiated by selecting a set of analysis results to bemined at a process 904. For example, the streamlined data set extractedusing the data extraction algorithm 800 can be selected at the process904. After the set of analysis results are selected at process 904,analysis data files corresponding to the selected set of analysisresults are opened at a process 908.

Still referring to FIG. 9, after opening the selected set of analysisresults at the process 908, the data mining algorithm 900 proceeds to aprocess 912 for defining a set of design criteria to be applied to theset of analysis results selected at the process 904. The set of designcriteria can include, in some embodiments, a plurality of thermodynamiccriteria. For example, referring to the example of TABLE 1, the set ofdesign criteria can include the phase equilibrium parameters (e.g.,weight percentage of NbC) in the first row that are within predeterminedtarget values. In other embodiments, the set of design criteria can alsoinclude economic criteria such as a cost per unit weight of the alloycomposition represented by the analysis result.

Still referring to FIG. 9, the set of design criteria is then applied ata process 916 to create a subset of analysis results representing asubset of the original set of data analysis results selected at process904. In some implementations, at the process 916, analysis resultscorresponding to alloys that do not meet the design criteria can beremoved (i.e., electronically deleted) from the analysis resultsselected at the process 904 such that the removed alloys are no longeranalyzed in subsequent processes of the data mining algorithm 900. Inother implementations, at least some analysis results corresponding toalloy compositions that do not meet the design criteria are notremoved/deleted, such that they remain within the subset of analysisresults.

Still referring to FIG. 9, the data mining algorithm 900 additionallyincludes a process 920 for defining a set of ranking criteria to beapplied to the subset of analysis results created in the process 912.The ranking criteria can be, for example, a set of criteria that may beweighted to generate an overall score based on the relative importanceof each of the criteria. Based on the ranking criteria defined at theprocess 920, a ranked subset of analysis results can be generated at aprocess 924, whose results can be printed (electronically on a screen ora data file or physically on paper) at a process 928. An example of aprint-out may include the printed ranked subset of analysis results in aform of a spread sheet whose rows are ordered in the order of decreasingscore based on the weighted criteria. Another example of a print-out mayadditionally rank the columns in the order of the weight of each of theranking criteria. For example, the first row of the spread sheet canlist the highest ranked alloy having the highest overall score based onthe weighted ranking criteria and the first column can represent theranking criteria having the highest relative importance. Once theprint-out is generated, the data mining algorithm ends at a process 932.

In some embodiments, the data mining algorithm 900 can be configured tobe relatively open such that it can take additional input at variousprocesses of the algorithm 900 in addition to the process 904 forselecting the set of analysis results. In these embodiments, a user cancreate new sub-routines and mimic a skilled person trained in the art ofmetallurgy evaluating a series of individual phase diagrams for alloydesign. Such a technique is not only useful in designing alloys incomplex systems, but can also useful in understanding and determiningrelationships between thermodynamic criteria and actual alloyperformance.

It will be appreciated that the amount of data obtained for a typicalcalculation is prohibitive to calculate and handle without employing thedata mining algorithm 900 implemented in an electronically implementedsystem including a processor, as described herein. This can beillustrated using the example presented earlier in connection with thephase diagram calculation algorithm 700 (FIG. 7) where 14,641 alloyshave been calculated and stored as individual data files, whose fileshave been further evaluated using the data extraction algorithm 800(FIG. 8) to produce a single spreadsheet. In this example, the datamining sub routine opens and evaluates a single spreadsheet whichcontains 14,641×5 (5 different thermodynamic criteria)=73,205 datapoints. The computerized method described herein can open the singlespreadsheet with 73,205 data point and perform the data mining algorithm900 practically instantaneously, whereas without such a method, theprocesses can take hours to days.

It will be understood that the overall computation including executionsof the phase diagram calculation algorithm 700 (FIG. 7), the dataextraction algorithm 800 (FIG. 8), and the data mining algorithm (FIG.9) can be managed such that a desired balance is struck between theoverall speed of the computation and the available computationalresources. FIG. 10 is a flow chart illustrating a method 1000 ofmanaging the overall computation including executing the phase diagramcalculation algorithm 700 (FIG. 7), the data extraction algorithm 800(FIG. 8), and the data mining algorithm 900 (FIG. 9). The method 1000includes a process 1004 of running one or more instances of the phasediagram calculation algorithm 700 (FIG. 7), either in series or inparallel. That is, one or more instances of the phase diagramcalculation algorithm 700 can be run serially over a period of time on asingle electronically implemented system, or alternatively, over ashorter period of time on a plurality of electronically implementedsystems.

Subsequent to running the one or more instances of the phase diagramcalculation algorithm 700 at the process 1004, the method 1000 proceedsto a decision process 1008 for determining whether or not results from adesired number of instances of phase diagram calculation algorithm 700have accumulated. Upon determination at the decision process 1008 thatthe results from the desired number of instances have not accumulated,the method 1000 loops back to the process 1004 to run additional one ormore instances of phase diagram calculation algorithm 700. On the otherhand, upon determination at the decision process 1008 that the resultsfrom the desired number of instances have accumulated, the method 1000proceeds to a process 1012 of running one or more instances of the dataextraction algorithm 800 (FIG. 8), which can be run either in series orin parallel, similar to the process 1004.

Subsequent to running the one or more instances of the data extractionalgorithm 800 at the process 1012, the method 1000 proceeds to adecision process 1016 for determining whether or not results from adesired number of instances of data extraction algorithm 1000 haveaccumulated. Upon determination at the decision process 1016 that theresults from the desired number of instances have not accumulated, themethod 1000 loops back to the process 1012 to run additional one or moreinstances of data extraction algorithm 800. On the other hand, upondetermination at the decision process 1016 that the results from thedesired number of instances have accumulated, the method 1000 proceedsto a process 1020 of running one or more instances of the data miningalgorithm 900 (FIG. 9), which can be run either in series or inparallel, similar to processes 1004 and 1012.

Whether a particular algorithm is run in series or in parallel, andwhether a particular algorithm will be run on results from a previousalgorithm on a rolling basis or in a single instance can be determinedbased on the estimated computation resources for the algorithms suchthat the overall design of the alloy is optimized for the desiredthroughput based on the computational resources available.

In the following, an example implementation of the method of FIG. 10 isdescribed for illustrative purposes. The initial set of alloycompositions to be calculated for one particular example may include,for example, 10,000 alloy compositions. Referring to FIG. 10, at process1004, the phase diagram calculation algorithm 700 can set to be executedfor the 10,000 compositions, for example, in 10 separate instances on 10electronically implemented systems, where each electronicallyimplemented system executes one instance of phase diagram calculationalgorithm 700 for 1000 compositions, for example. The process loop1004-1008 can be further configured to accumulate results from all 10instances of the phase diagram calculation algorithm 700. Aftercompletion of each of the 10 instances from one of the tenelectronically implemented system, the method 1000 determines at thedecision process 1008 whether all 10 instances of the phase diagramcalculation algorithm 700 have been run. Upon determining that less thanall 10 instances have been run, the method 1000 loops back to theprocess 1004 to run additional instances of the phase diagramcalculation algorithm 700 until all 10 instances have been executed, atwhich point the method 1000 proceeds to the process 1012 to run one ormore instances of the data extraction algorithm 800. The results of the10 instances executed in the process loop 1004-1008 can be organized,for example, as data structure including 10 folders, where each folderincludes the results of one instance of the phase diagram calculationalgorithm 700 from each electronically implemented system.

In the one particular example implementation of the method of FIG. 10,the results of all 10 instances of the phase diagram calculationalgorithm 700 can be executed as a single instance of the dataextraction algorithm 800. In addition, as an example, 700 differentthermodynamic criteria may be selected to be evaluated (e.g., at process804 in FIG. 8) for each of the results of phase diagram calculationalgorithm 700 for the 10,000 alloy compositions. The output of the dataextraction algorithm 800 can include, in this example, a spread sheethaving 10,000 rows (e.g., 1 for each alloy) and 101 columns (e.g., 1 tospecify each alloy and e.g., 100 to specify the 100 thermodynamiccriteria). Of course, while in this example, only one instance of thedata extraction algorithm 800 was specified to be run, if more than oneinstances of the data extraction algorithm 800 was specified to be run,the method 1000 determines at the decision process 1016 whether allspecified instances of the data extraction algorithm 800 has been run,and if there are additional instances remaining to be run, the method1000 loops back to the process 1012 to run the additional instances ofthe data extraction algorithm 800, until all specified instances havebeen run, at which point the method 1000 proceeds to a process 1020 torun one or more instances of the data mining algorithm 900.

In the one particular example implementation of the method of FIG. 10,the results of the one instance of the data extraction algorithm 800 canbe executed at the process 1020 as multiple instances, in series or inparallel, of the data mining algorithm 900. For example, the multipleinstances of the data mining algorithm 900 can represent ranking the10,000 alloy compositions (e.g., at the process 924 in FIG. 9), fordesigning non-magnetic alloys, crack resistant hardfacing alloys, andcorrosion resistant alloys.

It will be appreciated that, by the method described in FIG. 10 and theexample implementation thereof, once the results from the 1012-1016process loop (e.g., the spreadsheet with 10,000 columns and 101 rows inthis example) is generated, it can used to design multiple types ofalloys for different purposes (e.g., non-magnetic alloys, crackresistant hardfacing alloys, and corrosion resistant alloys), by simplyexecuting subsequent instances of the data mining algorithm 900 (FIG. 9)at the process 1020 (FIG. 10) without having to repeatedly execute thephase diagram calculation algorithm 700 and the data extractionalgorithm 800.

EXAMPLES Example 1

Selecting a non-magnetic hardbanding alloy composition This exampledetails an alloy design routine that can be used to develop alloycompositions which are both non-magnetic and possess a high wearresistance and hardness. Such properties are not inherently contained inFe-based materials, as the non-magnetic form of austenite is the softestform of iron. Thus, this challenging dual property material is a goodcandidate for demonstrating the capability of the described designconcept, to illustrate the through investigation involved in the designof complex multi-component alloy systems. It was determined using aseparate inventive process involving a comparison of experimentation andmodeling results by one skilled in the art that the FCC-BCC transitiontemperature and the total hard particle phase fraction at 1300K were twothermodynamic criteria that can be used advantageously for designingalloys in this application space. Furthermore, it was determined by thisseparate inventive process that having a minimum FCC-BCC transitiontemperature of 950 K and a minimum hard particle phase fraction of 20mole % were also advantageous for ensuring that such alloys had a highprobability of meeting the performance requirements of this applicationspace.

It can be appreciated that development of the T(γ→α) thermodynamicquantity required an experimental correlation process to define. It canbe appreciated that it is not inherently obvious to suggest that thedesign of a non-magnetic hardbanding alloy composition for roomtemperature applications would involve selecting an alloy which a phasediagram would suggest is magnetic at room temperature. However, aFCC-BCC transition temperature above room temperature means that themagnetic phase (BCC) of iron is thermodynamically stable at roomtemperature. This example illustrates that the phase diagram itself doesnot obviously contain the information useful for alloy design, ratherthis method often leads to the creation of thermodynamic quantitieswhich are non-obvious or even counter to conventional metallurgicalassumptions.

TABLE 2 represents the results of 11 instances of a phase diagramcalculation algorithm similar to the phase diagram calculation algorithm700 of FIG. 7. A description of the parameters used to run thesesub-routines is shown in TABLE 2, including the minimum calculationrange (min), maximum calculation (range), and step size (step) are shownfor each element as well as the temperature. In each calculation seriesthere are some elements which are held constant (at set values)throughout the sub-routine. The 11 instances of the phase diagramcalculation algorithm generated 4,408 individual alloy data files.

TABLE 2 Series No. B C Cr Mn Nb Ni Ti V W Fe Temp 1 Set 1 10 4 0.2 0.5 5Bal Min 1.5 2 0 200 Max 3 18 10 2000 Step 0.5 2 2 50 2 Set 1 10 4 0.20.5 5 Bal Min 1.5 2 0 200 Max 3 18 10 2000 Step 0.5 2 2 50 3 Set 18 5 100.2 0.5 Bal Min 0 0 0 1 200 Max 1 2 4 5 2000 Step 0.2 2 2 1 50 4 Set 118 10 0.2 0.5 5 Bal Min 0 0 0 200 Max 2 10 4 2000 Step 0.5 1 1 50 5 Set18 5 10 0.2 0.5 Bal Min 0 0 0 1 200 Max 1 2 4 5 2000 Step 0.2 2 2 1 50 6Set 0.2 0.5 Bal Min 0 0 0 0 0 0 200 Max 3 20 10 4 10 4 2000 Step 0.75 105 2 5 2 50 7 Set 3 10 0.2 0.5 Bal Min 10 0 0 0 200 Max 20 4 10 4 2000Step 10 2 5 2 50 8 Set 3 0.2 0.5 Bal Min 10 0 0 0 0 200 Max 20 5 4 10 42000 Step 10 5 2 5 2 50 9 Set 1 18 10 4 Bal Min 0 0 0 200 Max 6 6 4 2000Step 2 2 2 50 10 set 18 10 4 Bal Min 0 1.5 0 0 0 200 Max 1 3 6 6 4 2000Step 0.5 0.5 2 2 2 50 11 Set 1 6 4 0.2 0.5 Bal Min 1.5 0 4 5 200 Max 3 410 15 2000 Step 0.5 2 2 5 50

Subsequently, a data extraction algorithm similar to the data extractionalgorithm 800 of FIG. 8 was applied the results of the phase diagramcalculation algorithm shown in TABLE 2. The data extraction algorithmwas executed on all 4,408 alloy compositions initially calculated in themultiple instances of phase diagram calculation algorithm. Theindividual alloy data files were evaluated for the followingthermodynamic criteria: (1) FCC-BCC transition temperature as defined bythe highest temperature at which BCC Fe exists as a non-zero quantity;and (2) hard particle phase fraction at 1300 K as defined by the molephase fraction sum of any carbides, borides, or intermetallics presentat 1300 K in the alloy at equilibrium. At the conclusion of the dataextraction algorithm, a single data file was generated tabulating theFCC-BCC transition temperature and hard particle phase fraction for eachof the 4,408 alloy compositions.

Subsequently, a data mining algorithm similar to the data miningalgorithm 900 of FIG. 9 was applied to the result of the data extractionalgorithm described above. As mentioned, based on a separate inventiveprocess it was determined that that a minimum FCC-BCC transitiontemperature (T_(γ→α)) of 950 K and a minimum hard particle phasefraction (Σ_(hard)) of 20 mole % were advantageous criteria for ensuringthat such alloys had a high probability of meeting the performancerequirements of this application space. Thus, two required designcriteria were defined: (T_(γ→α))>9950 K and (Σ_(hard))>20 mol %. Basedon this filter, 643 alloys remained within the preferred design subset.Next, a ranking design criteria was defined: alloys were rankingaccording to (Σ_(hard)) with higher hard particle phase fractions beingconsidered more favorable.

TABLE 3 represents a portion of an example of a final output of the datamining algorithm in a single table format having alloy compositions thatare likely to be non-magnetic and possess a high hardness and wearresistance. Alloys are further organized in the data file according tothe level of probable hardness and wear resistance. The alloycompositions listed in TABLE 3 represent those that are likely to be thehardest and most wear resistant alloys of the preferred subset:

TABLE 3 Fe B C Cr Mn Nb Ni Ti V W T_(γ→α) Σ_(hard) 60.5 1 2.5 18 10 4 00 0 4 950 52% 74.3 1 3 6 4 0 6 0.2 0.5 5 950 52% 72.3 1 3 6 4 0 8 0.20.5 5 900 52% 76.3 1 3 6 4 0 4 0.2 0.5 5 950 51% 71.3 1 3 6 4 0 4 0.20.5 10 950 51%

TABLE 3 demonstrates a small example of the ability to design an alloywithout the need for one skilled in the art to evaluate thermodynamicinformation. This is an example of a table which simply links alloycompositions to two thermodynamic quantities. Such a table can contain alarge number of unique alloys and a large number of unique thermodynamicquantities. Alloy design is then executed utilizing purely algorithmicsorting and ranking methods. In the above example the alloy at the topof the chart Fe_(60.5)B₁C_(2.5)Cr₁₈Mn₁₀Nb₄W₄ is the output of the fullalloy design process, and is simply an alloy composition. The user didnot need to evaluate phase diagrams or any graphical thermodynamicdisplays in order to identify this alloy. Furthermore, the user did notneed to understand any correlation between alloy composition and desiredperformance, the algorithm simply identified the best candidate out ofthe dataset via purely objective numerical analysis.

Example 2 Selecting a Crack-Resistant Hardfacing Alloy Composition

This example details an alloy design routine that can be used to developalloy compositions which simultaneously have high wear resistance andare very resistant to cracking. Such properties are not inherentlycontained in Fe-based materials, as hardness and toughness (whichprovides resistance to cracking) are two properties known to thoseskilled in the art of metallurgy to be inversely related. Thus, thischallenging dual property material is a good candidate for demonstratingthe capability of the described design concept, to illustrate thethorough investigation involved in the design of complex multi-componentalloy systems. It was determined using a separate inventive processinvolving a comparison of experimentation and modeling results by oneskilled in the art that the total primary hard particle phase fractionand the total secondary hard particle phase fraction were twothermodynamic criteria that can be used advantageously for designingalloys in this application space. Furthermore, it was determined by thisseparate inventive process that a minimum primary hard particle phasefraction of 2 mole % and a maximum secondary hard particle phasefraction of 10 mole % were the required thresholds for ensuring thatsuch alloys had a high probability of meeting the performancerequirements of this application space.

TABLE 4 represents the results of 13 instances of a phase diagramcalculation algorithm similar to the phase diagram calculation algorithm700 of FIG. 7. A description of the parameters used to run thesesub-routines is shown in TABLE 4, including the minimum calculationrange (min), maximum calculation (range), and step size (step) are shownfor each element as well as the temperature. In each calculation seriesthere are some elements which are held constant (at set values)throughout the sub-routine. These 13 instances of the phase diagramcalculation algorithm generated 9,132 individual alloy data files.

TABLE 4 Series No. B C Cr Mn Mo Nb Si Ti Fe Temp 1 Set 0 5.04 1.16 0.740.76 Bal Min 0.5 0 0 200 Max 2.5 10 10 2000 Step 0.5 2 2 50 2 Set 1.075.04 1.16 0.74 0.76 Bal Min 0 0 0 200 Max 2 10 10 2000 Step 0.5 2 2 50 3Set 5.04 1.16 0.74 0.76 3 Bal Min 0 0 0 200 Max 2 2.5 10 2000 Step 0.50.5 2 50 4 Set 1.16 0.74 0.76 3 Bal Min 0 3 0 4 200 Max 2 2.5 10 10 2000Step 0.5 0.5 2 2 50 5 Set 2.5 1.16 0.74 0.76 Bal Min 0 0 4 0 200 Max 2 410 10 2000 Step 0.5 2 2 2 50 6 Set 2.5 1.16 0.74 0.76 Bal Min 0 0 4 0200 Max 2 4 10 10 2000 Step 0.5 2 1 2 50 7 Set 1.16 0.74 0.76 Bal Min2.5 0 0 200 Max 5 10 10 2000 Step 0.5 2 2 50 8 Set 0 1.16 0.74 0.76 BalMin 0 1 0 0 200 Max 1.5 2.5 5 5 2000 Step 0.5 0.5 1 1 50 9 Set 0 1.160.74 0.76 Bal Min 2.5 4 0 200 Max 5 10 10 2000 Step 0.5 2 1 50 10 Set 01.16 0.74 0.76 Bal Min 0 2.5 4 0 200 Max 2 5 10 10 2000 Step 0.5 0.5 2 250 11 Set 0 1.16 0.74 0.76 Bal Min 0.8 0 0 200 Max 1.6 5 5 2000 Step 0.21 1 50 12 Set 0 1.16 0.74 0.76 Bal Min 0 0.8 0 0 200 Max 1 2.6 5 5 2000Step 0.2 0.2 1 1 50 13 Set 0 1.16 0.74 0.76 Bal Min 0 0.8 6 6 200 Max 12.6 10 10 2000 Step 0.2 0.2 1 1 50

Subsequently, a data extraction algorithm similar to the data extractionalgorithm 800 of FIG. 8 was applied the results of the phase diagramcalculation algorithm shown in TABLE 4. The data extraction algorithmwas executed on all 9,132 alloy compositions initially calculated in themultiple data calculation sub-routines. The individual alloy data fileswere evaluated for the following thermodynamic criteria: (1) primaryhard particle phase fraction as defined by the mole phase fraction sumat room temperature of any carbides, borides, or intermetallic phaseswhich exist at a non-zero quantity at a temperature at least 10K abovethe highest temperature at which austenitic iron exists as a non-zeroquantity; and (2) secondary hard particle phase fraction as defined bythe mole phase fraction sum at room temperature of any carbides,borides, or intermetallic phases which exist at a non-zero quantity at atemperature less than 10K above the highest temperature at whichaustenitic iron exists as a non-zero quantity. At the conclusion of thedata extraction algorithm, a single data file was generated tabulatingthese 2 thermodynamic quantities for each of the 9,132 alloycompositions.

Subsequently, a data mining algorithm similar to the data miningalgorithm 900 of FIG. 9 was applied to the result of the data extractionalgorithm described above. As mentioned, based on a separate inventiveprocess it was determined that a minimum primary hard particle phasefraction (primary) of 2 mole % and a maximum secondary hard particlephase fraction (secondary) of 10 mole % were the required thresholds forensuring that such alloys had a high probability of meeting theperformance requirements of this application space. Thus, two requireddesign criteria were defined: primary >2% and secondary <10 mol %. Basedon this filter, 341 alloys remained within the preferred design subset.Next, a ranking design criteria was defined: alloys were rankingaccording to (Primary) with higher primary hard particle phase fractionsbeing considered more favorable. TABLE 5 represents a portion of anexample of a final output of the data mining algorithm.

TABLE 5 Fe B C Cr Mn Mo Nb Si Ti Primary Secondary 72.8 2 2.5 0 1.160.74 10 0.76 10 33.9% 0.9% 70.8 2 2.5 2 1.16 0.74 10 0.76 10 33.7% 5.2%73.8 2 2.5 0 1.16 0.74 9 0.76 10 32.9% 2.1% 74.8 2 2.5 0 1.16 0.74 80.76 10 31.9% 3.1% 72.8 2 2.5 2 1.16 0.74 8 0.76 10 31.3% 5.8%

Example 2 is a good illustration of the extraction method and thenecessity to have a special algorithm and calculation routine built toextract thermodynamic information from a phase diagram which is notinherently obvious or present in the thermodynamic phase diagram itself.In this example, primary and secondary hard particles are differentiatedbased on the formation temperature of the phases themselves inrelationship to the formation temperature of the steel phase, austeniteor ferrite. Given the number of potential hard phases that arepotentially present when calculating 9,000 alloys, a relatively complexalgorithm must be constructed to properly extract this information. Inother words, the thermodynamic quantity labelled as ‘Primary’ is createdusing a sophisticated algorithm which interrogates a phase diagram, butultimately is simply a number. The thermodynamic quantity labelled‘Secondary’ is similarly extracted. These two example illustrate thatthe thermodynamic phase diagram is being utilized to create a separateand unique database which can be effectively mined at a later stage.

To illustrate the inherent complexity of the thermodynamic quantity‘Primary’ a description of the algorithm to generate this number isprovided. First, the algorithm determines whether austenite or ferriteis the Fe-based phase which is present at the highest temperature. Thehighest temperature at which either of these two phases is present isdetermined to be temperature 1. Second, the algorithm determines all ofthe other phases present over the temperature range of calculation.Thirdly, the formation temperature of each of the ‘other phases’ isdetermined and recorded. The formation temperature is defined as thehighest temperature at which the specified phase has a non-zero molefraction. Fourthly, the algorithm evaluates whether the formationtemperature for each of the ‘other phases’ is greater than temperature1. If the formation temperature is higher, the phase is regarded as aprimary hard phase. If the formation temperature is lower, the phase isregarded as a secondary hard phase. Fifthly, the primary carbides molefractions at a specified temperature of 300 K are summed up andextracted into a database under the column descriptor ‘Primary’. It canbe appreciated that the thermodynamic quantities are not merely numbersinherently present or obviously displayed in a phase diagram, but arerather products of complex algorithms required for the purposes of alloydesign.

The above two examples show the three described steps proceeding in alinear fashion: calculation, extraction, and mining. However, asdescribed previously one unique aspect of this invention above simplyusing CALPHAD via computer is the ability to design multiple alloys ofunique microstructure and performance from the database of extractedthermodynamic quantities. For example, roughly 15,000 alloys werecalculated in the above two examples and two thermodynamic quantitieswere described in each of the extraction steps. However, in this methodit is advantageous to extract the full spectrum of the potentialthermodynamic quantities during the extraction routine regardless of theintended design of the metallurgist at the time.

In the above two examples, a non-magnetic hardfacing material and acrack resistant hardfacing material were separately designed using thefull 3 step process, calculation, extraction, and mining. In theproceeding examples, additional independent alloys can be designedwithout running additional calculations. This example shows theeffectiveness of this method, whereby a metallurgist can executeuniquely separate design routines without running additionalcalculations or interfacing with phase diagrams.

Example 3 Selecting a Corrosion and Abrasion-Resistant Alloy Composition

Utilizing the previous 15,000 calculations, a metallurgist canimmediately mine this data to develop a unique alloy system: abrasionand corrosion resistant hardfacing alloys. In this example, theextraction routine is rerun on the 15,000 alloys to include additionalthermodynamic properties of interest that one skilled in the art hasdetermined to be relevant to the desired microstructure and propertiesvia experimental measurements. An example of an additional thermodynamicparameter to be added would be the Cr content in weight % in theaustenite phase at 1300K, termed ‘1300K Austenite Cr’. Again theselection of this thermodynamic quantity is non-obvious and requiresexperiments in that the corrosion performance of the alloy is beingcorrelated to the Cr content at high temperature (1300K) in a phasewhich does not exist in the alloy at room temperature (austenite). Inthis example, no calculation routine is run, and 15,000 alloys can bequickly interrogated for a unique alloy system. In example 3, the dataextraction step is run to extract all of the thermodynamic quantitiesdiscusses thus far, T(γ−α), Σ_(hard), primary, secondary, and 1300Austenite Cr even though not all of these quantities are relevant tothis particular example. Once extracted this data can be mined todetermine the best alloy for this application. For example, all 15,000alloys can be sorted to immediately remove any alloy which has a 1300Austenite Cr level below 0.12. Then the remaining alloys can be rankedaccording to the highest secondary value. FIG. 9 is displays theextracted thermodynamic quantities for all 15,000 alloys to demonstratehow the design of such an alloy is quantified into simple numericalterms. However, as mentioned, no graphical display or evaluation ofthermodynamic information is required to make this alloy design. Rather,a single alloys or collection of several alloys is selected formanufacture based on the algorithmic sorting and ranking routines.

The power of this method is revealed in that this alloy design wasexecuted on 15,000 alloys without having to recalculate 15,000 alloys,which may take up to about 15 hours using a supercomputer. In the designprocess of Example 3, only the extraction process was run which may takeup to around 1 hour. It can be appreciated that this method can be usedto avoid prohibitive lengths of time such as 15,000 hours of calculationtime for 15,000,00 alloys, allowing for this extremely large alloy setto be utilized in alloy design in about 100 hours.

Referring back to FIG. 6A, the chart 600 depicts the simultaneousevaluation of many alloys using two thermodynamic quantitiessimultaneously. In part this is done for matter of convenience becauseit is physically impossible to graphically display a series of alloys ona two dimensional plot for more than 2 thermodynamic quantities.However, this method is advantageous in its unique ability to evaluate alarge set of alloys for more than 2 thermodynamic quantities. Thenumerical sorting and ranking algorithms allow an infinite number ofthermodynamic quantities to be simultaneously considered, because at nopoint must a metallurgist review a phase diagram or other graphicaldisplay. It is often the case that multiple performance criteria must bemet for the alloy to have utility as a manufactured product.

Example 4 Selecting a Non-Magnetic and Crack Resistant Alloy Composition

In another example the previous 15,000 calculations can be againutilized. In this case, the extraction routine is also avoided due tothe extraction of the 5 thermodynamic criteria in the previous exampledespite only requiring two quantities for the design of the corrosionand abrasion resistant alloy. As thermodynamic criteria are continuouslydeveloped in this method, the calculation and extraction methods can bemore often avoided to speed the process of alloy design. Referring backto FIG. 6B, the chart 620 illustrates an example chart used in mining acrack resistant non-magnetic hardbanding material. In this example, itwas determined in a separate inventive step that the primary hard phasefraction and the T α to γ transition temperature were relevantthermodynamic parameters for designing this product. The chart 620 ofFIG. 6B then represents the mining results of the 15,000 alloyscalculated in this particular example whereby these alloys are nowevaluated for the non-magentic hardbanding application. In example 4,this uniquely and separate alloy design process was executedinstantaneously as no additional calculation or extraction algorithmswere run. Sorting and ranking are essentially instantaneous to the usereven when designing within a very large number of alloys. It canappreciated in this example how an extremely large alloy set of15,000,000 alloys can be utilized in alloy design in a matter of secondsusing this disclosed method, whereas conventional CALPHAD techniqueswould require a prohibitively long 15,000 hrs (625 days or 1.7 years).However, this comparison is incorrect in that it requires one skilled inthe art to evaluate and understand thermodynamic information containedin 15,000,000 phase diagrams, which cannot be executed via conventionalCALPHAD. Thus, it can be appreciated that the simultaneous evaluation of15,000,000 alloys via conventional CALPHAD methods is logisticallyimpossible.

Similar to FIG. 6A, FIG. 6B depicts the simultaneous evaluation of manyalloys using just two thermodynamic quantities due to the physicallimitations of plotting multiple variables. However, it is oftendesirable to use 3 or more thermodynamic quantities in alloy design andExample 4 can further benefit from the use of additional thermodynamicquantities in its design. In Example 4 the ranking and sortingalgorithms can be used to identify an alloy which contains a maximum ‘Tα to γ’ threshold and which are further ranking according to the highest‘Primary’ quantity. The design of the alloy can be further enhanced inthis example by adding an additional criteria, ‘Secondary’. In thiscase, the ‘Secondary’ quantity is sorted such that only alloys whichhave a maximum ‘Secondary’ quantity are further considered in thedesign. In this exemplary example, three thermodynamic criteria aresimultaneously used in the design of the alloy set. It can beappreciated that is impossible to create a thermodynamic display ofinformation using conventional CALPHAD methods whereby 3 independentvariables can be used in design; such a display must be a threedimensional image and is prohibitively difficult to interpret.Furthermore, the physical display of more than three independentvariables cannot be physically displayed. The disclosed method is theonly known way to execute alloy design using more than 3 thermodynamicquantities simultaneously for a plurality of alloys.

In one embodiment, this method is used to evaluate 2 or morethermodynamic quantities of an alloy set. In a preferred embodiment,this method is used to evaluate 3 or more thermodynamic quantities of analloy set. In a still preferred embodiment, this method is used toevaluate 4 or more thermodynamic quantities of an alloy set.

Example 3 and 4 highlight another unique characteristic of this methodas compared to conventional CALPHAD, the capability to execute alloydesign amongst vast compositional ranges effectively. In one embodiment,this method is unique in its ability to execute alloy design using 100alloys simultaneously. In a preferred embodiment, this method is uniquein its ability to execute alloy design using 500 alloys simultaneously.In a still preferred embodiment, this this method is unique in itsability to execute alloy design using 1,000 alloys simultaneously. Inthe US 2009/0053100 A1 example, the CALPHAD method is used toeffectively evaluate 1-4 alloys simultaneously. Conventional techniquesusing graphical displays of thermodynamic information are effective atevaluating 1-10 alloys, become increasingly ineffective when evaluating11-99 alloys simultaneously, and become useless for alloy design whenevaluation 100 alloys or more.

In another example, 1,000,000 alloys have been calculated and 50thermodynamic criteria have been defined based on experimentalmeasurements and their ability to predict microstructure andperformance. Once calculated, which may take up to 6 months using asuper computer, and extracted, which may take up to several weeks, the,mining process can be executed to design multiple types of alloys. Themining process is essentially instantaneous utilizing a computer.

In another example, all possibilities of steel alloys, which representstrillions of potential alloy combinations, are calculated which may takeup to several years utilizing a series of supercomputers. 100 relevantthermodynamic quantities are determined via 100 unique inventive processto predict a variety of microstructural and performance characteristicsin steel. Once calculated and evaluated, this data can be mined and usedto design alloys for a variety of different desired microstructural andperformance criteria to develop unique and separate functional materialsamongst the entire span of possible steels effectively instantaneously.

In a final example, all possible elemental combinations are calculatedwhich may take up to a decade utilizing a series of supercomputers.1,000 relevant thermodynamic quantities are determined via 1,000 uniqueinventive process to predict a variety of microstructural andperformance characteristics. Once calculated and evaluated, this datacan be mined and used to design alloys for a variety of differentdesired microstructural and performance criteria to develop unique andseparate functional materials amongst the entire span of possiblematerials effectively instantaneously.

Features, materials, characteristics, or groups described in conjunctionwith a particular aspect, embodiment, or example are to be understood tobe applicable to any other aspect, embodiment or example describedherein unless incompatible therewith. All of the features disclosed inthis specification (including any accompanying claims, abstract anddrawings), and/or all of the steps of any method or process sodisclosed, may be combined in any combination, except combinations whereat least some of such features and/or steps are mutually exclusive. Theprotection is not restricted to the details of any foregoingembodiments. The protection extends to any novel one, or any novelcombination, of the features disclosed in this specification (includingany accompanying claims, abstract and drawings), or to any novel one, orany novel combination, of the steps of any method or process sodisclosed.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of protection. Indeed, the novel methods and systems describedherein may be embodied in a variety of other forms. Furthermore, variousomissions, substitutions and changes in the form of the methods andsystems described herein may be made. Those skilled in the art willappreciate that in some embodiments, the actual steps taken in theprocesses illustrated and/or disclosed may differ from those shown inthe figures. Depending on the embodiment, certain of the steps describedabove may be removed, others may be added. Furthermore, the features andattributes of the specific embodiments disclosed above may be combinedin different ways to form additional embodiments, all of which fallwithin the scope of the present disclosure.

Although the present disclosure includes certain embodiments, examplesand applications, it will be understood by those skilled in the art thatthe present disclosure extends beyond the specifically disclosedembodiments to other alternative embodiments and/or uses and obviousmodifications and equivalents thereof, including embodiments which donot provide all of the features and advantages set forth herein.Accordingly, the scope of the present disclosure is not intended to belimited by the specific disclosures of preferred embodiments herein, andmay be defined by claims as presented herein or as presented in thefuture.

What is claimed is:
 1. A method of visually outputting one or more alloycompositions having one or more target properties, the methodcomprising: extracting programmatically by a computing device one ormore thermodynamic quantities from thermodynamic phase data for each ofa plurality of alloy compositions comprising at least four alloyingelements, wherein the thermodynamic phase data is calculated byindependently varying an amount of each of three of the at least fouralloying elements over a specified composition range; and simultaneouslyvisually representing the plurality of alloy compositions by the one ormore thermodynamic quantities, wherein the one or more thermodynamicquantities are predetermined to be correlated to the one or more targetproperties.
 2. The method of claim 1, wherein simultaneously visuallyrepresenting the plurality of alloy compositions comprises plotting in atwo or three dimensional graph in which each of the thermodynamicquantities represents an axis, and wherein each of data pointsrepresents one of the plurality of alloy compositions.
 3. The method ofclaim 1, wherein extracting comprises extracting two or morethermodynamic quantities from the same thermodynamic phase data.
 4. Themethod of claim 1, wherein the one or more thermodynamic quantities areselected from the group consisting of an amount of an alloy phase at atemperature, a phase transition temperature between two alloy phases, anamount of an alloying element in an alloy phase at a temperature and anamount of an alloy phase at an alloy phase transition temperature. 5.The method of claim 1, further comprising sorting or filtering the alloycompositions programmatically by the computing device using amicroprocessor according to one or more predetermined numerical criteriaof the one or more thermodynamic quantities.
 6. The method of claim 5,wherein sorting or filtering comprises eliminating from an entire set ofthe alloy compositions one or more alloy candidate compositions based onone or more numerical criteria selected from a minimum thresholdthermodynamic quantity, a maximum threshold thermodynamic quantity and arange between a minimum threshold thermodynamic quantity and a maximumthreshold thermodynamic quantity.
 7. The method of claim 1, furthercomprising: extracting one or more thermodynamic quantities a secondtime from the same thermodynamic phase data of each of the of alloycompositions without additional thermodynamic phase data, wherein theone or more thermodynamic quantities and the one or more thermodynamicquantities extracted the second time are predetermined to be correlatedto different target properties; and simultaneously representing theplurality of alloy compositions a second time by the one or morethermodynamic quantities extracted the second time.
 8. The method ofclaim 1, further comprising electronically mining the extractedthermodynamic quantities programmatically by the computing device usinga microprocessor to rank at least a subset of the plurality of alloysbased on a comparison of at least a subset of the thermodynamicquantities.
 9. The method of claim 8, wherein electronically miningcomprises mining the extracted thermodynamic quantities a plurality oftimes to rank different subsets of the plurality of alloys for differenttarget properties without calculating additional thermodynamic phasedata or additionally extracting thermodynamic quantities therefrom. 10.An apparatus configured to visually output one or more alloycompositions having one or more desired target properties, the apparatuscomprising: a computing device comprising a microprocessor; athermodynamic phase data extraction module configured to extractprogrammatically by the computing device one or more thermodynamicquantities from thermodynamic phase data for each of a plurality ofalloy compositions comprising at least four alloying elements, whereinthe thermodynamic phase data is calculated by independently varying anamount of each of three of the at least four alloying elements over aspecified composition range; and an output module configured tosimultaneously visually represent the plurality of alloy compositions bythe one or more thermodynamic quantities, wherein the one or morethermodynamic quantities are predetermined to be correlated to the oneor more target properties.
 11. The apparatus of claim 10, wherein theoutput module is configured to simultaneously visually represent theplurality of alloy compositions by plotting a two or three dimensionalgraph in which each of the thermodynamic quantities represents an axis,and wherein the plotted data points represent the plurality of alloycompositions.
 12. The apparatus of claim 10, wherein the thermodynamicphase data extraction module is configured to extract two or morethermodynamic quantities from the same thermodynamic phase data.
 13. Theapparatus of claim 10, wherein the one or more thermodynamic quantitiesare selected from the group consisting of an amount of an alloy phase ata temperature, a phase transition temperature between two alloy phases,an amount of an alloying element in an alloy phase at a temperature andan amount of an alloy phase at an alloy phase transition temperature.14. The apparatus of claim 10, further comprising a mining moduleconfigured to sort or filter the alloy compositions programmatically bythe computing device using the microprocessor according to one or morepredetermined numerical criteria of the one or more thermodynamicquantities.
 15. The apparatus of claim 10, further comprising a miningmodule configured to electronically mine the extracted thermodynamicquantities programmatically by the computing device using themicroprocessor to rank at least a subset of the plurality of alloysbased on a comparison of at least a subset of the thermodynamicquantities.
 16. A non-transitory computer-readable medium comprisinginstructions stored thereon that when executed cause a computing deviceto perform steps for visually outputting one or more alloy compositionshaving one or more desired target properties, the steps comprising:extracting programmatically by a computing device one or morethermodynamic quantities from thermodynamic phase data for each of aplurality of alloy compositions comprising at least four alloyingelements, wherein the thermodynamic phase data is calculated byindependently varying an amount of each of three of the at least fouralloying elements over a specified composition range; and simultaneouslyvisually representing the plurality of alloy compositions by the one ormore thermodynamic quantities, wherein the one or more thermodynamicquantities are predetermined to be correlated to the one or more targetproperties.
 17. The non-transitory computer-readable medium of claim 16,wherein simultaneously visually representing the plurality of alloycompositions comprises plotting in a two or three dimensional graph inwhich each of the thermodynamic quantities is represented as an axis,and wherein plotted data points represent the plurality of alloycompositions.
 18. The non-transitory computer-readable medium of claim16, wherein extracting comprises extracting two or more thermodynamicquantities from the same thermodynamic phase data.
 19. Thenon-transitory computer-readable medium of claim 16, wherein the one ormore thermodynamic quantities are selected from the group consisting ofan amount of an alloy phase at a temperature, a phase transitiontemperature between two alloy phases, an amount of an alloying elementin an alloy phase at a temperature and an amount of an alloy phase at analloy phase transition temperature.
 20. The non-transitorycomputer-readable medium of claim 16, wherein the steps further comprisesorting or filtering the alloy compositions programmatically by thecomputing device using a microprocessor according to one or morepredetermined numerical criteria of the one or more thermodynamicquantities.
 21. The non-transitory computer-readable medium of claim 16,wherein the steps further comprise electronically mining the extractedthermodynamic quantities programmatically by the computing device usinga microprocessor to rank at least a subset of the plurality of alloysbased on a comparison of at least a subset of the thermodynamicquantities.